[{"data":1,"prerenderedAt":588},["ShallowReactive",2],{"uc-payment-investigations-and-exceptions":3,"uc-regulations":382},{"useCase":4,"evidence":185,"blitsAiDeployments":246,"benchmarks":247,"indicative":254,"related":257,"indexability":380,"includeUnpublished":191},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":27,"audience":31,"autonomy":32,"adoptionStage":33,"segment":31,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":48,"macroEstimates":83,"feasibility":84,"implementation":98,"risk":137,"blitsAi":161,"faq":163,"related":173,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI for payment investigations and exceptions","Payment investigations and exceptions","AI for payment exceptions and investigations","AI sorts failed payments, drafts ISO 20022 investigation messages and chases banks. BNY says a digital employee handles over 10% of its payment repair issues.","published","AI that works the payments that fall out of straight through processing: it reads the failure, repairs or enriches the message, drafts the ISO 20022 or SWIFT investigation, chases the counterparty bank and proposes a return, recall or correction, while an operator approves anything that moves money.",[12,13,14,15],"payment exceptions and investigations automation","payment repair AI","SWIFT case management automation","E&I automation",[17,18],"banking","payments",[20,21],"operations","customer-service",[23,24,25,26],"agentic-workflow","document-processing","classification-and-routing","content-generation",[28,29,30],"internal-tools","api","email","back-office","supervised-agent","emerging","A payment that flows straight through needs no manual work, but every payment that falls out\ndoes. A missing or malformed field, a name that does not match the account, a sanctions hit, a\nduplicate or a customer asking \"where is my payment\" each opens a case. Operators then read MT\nand MX messages, look up the payment in several systems, write free text queries to\ncorrespondent banks and wait for an answer that may arrive by message, email or not at all.\n\nMuch of this work is reading and writing: free format messages such as the MT199, emails and\ncustomer queries, while the customer keeps asking for news. ISO 20022 defines structured\nmessages for exceptions and investigations, such as the interbank payment cancellation request\n(camt.056) and its response (camt.029), the payment status request (pacs.028) and the\ninvestigation request and response (camt.110 and camt.111). The Committee on Payments and Market\nInfrastructures recommends that payment system operators and participants align with its\n[harmonised ISO 20022 data requirements](https://www.bis.org/cpmi/publ/d230.htm) for cross\nborder payments before the end of 2027, and expects correct account data to mean fewer\nexceptions and investigations. Structured data makes cases easier to classify; AI does the\nreading, drafting and chasing that remain.\n\nBanks have started with the repair step. In a\n[Microsoft customer story](https://www.microsoft.com/en/customers/story/27322-bny-microsoft-365-copilot),\nBNY says a digital employee in its Eliza platform repairs missing or incomplete payment\ninstructions and handles over ten percent of its payment repair issues around the world.",[],"1. **Classify the exception.** The agent reads the rejected or held payment, the error codes and\n   any inbound query (camt.056 recall, camt.110 investigation request, camt.029 or camt.111\n   response, gpi tracker status, free text MT199 or email) and assigns the case type.\n2. **Gather the facts.** It pulls the payment's lifecycle from the payment hub, the tracker and\n   the customer record, and retrieves the relevant scheme rules and internal procedures.\n3. **Repair or propose.** For repairable errors it proposes the corrected fields (for example a\n   BIC derived from the IBAN) with its reasoning. For investigations it proposes the next action:\n   request information, recall, return or beneficiary correction.\n4. **Draft the messages.** It drafts the structured investigation message to the counterparty and\n   the plain language update to the customer or the relationship manager.\n5. **Approve and chase.** An operator approves any repair, recall or credit adjustment. The agent\n   then sends, tracks deadlines, chases unanswered queries and closes the case with a full trail.",[38,39,40,41],"cost-to-serve","speed","customer-experience","risk-reduction",[43,44,45,46,47],"automation-rate","processing-time-reduction","handling-time-reduction","cycle-time-days","interactions-handled",{"referenceOrg":49,"inputs":50,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A regional bank handling 100,000 payment exception and investigation cases a year",[51,57,64,71],{"key":52,"label":53,"low":54,"high":54,"unit":55,"note":56},"cases","Exception and investigation cases per year",100000,"cases per year","The reference bank. Replace with your own case volume.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"minutesPerCase","Operator minutes per case today",20,45,"minutes per case","Editorial assumption covering reading, lookups, drafting and follow up. Replace with your own time study.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"effortReduction","Share of operator time the AI removes",0.25,0.5,"fraction of time per case","Editorial assumption; the AI drafts and gathers, a human still approves money movement.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"costPerHour","Fully loaded operations cost per hour",35,60,"USD per hour","Editorial assumption, replace with your own.","cases * minutesPerCase / 60 * effortReduction * costPerHour","USD","per year","Investigation effort avoided","Labour only. It leaves out fewer customer chasers, lower compensation and claim costs from faster resolution, and the cost of the platform and the integration with the payment hub.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":92},"high","Cases span the payment hub, sanctions filtering, the SWIFT interface, tracker data and customer systems, and every recall or repair moves or redirects money. Structured ISO 20022 case messages help, but many counterparties still answer in free text.",[88,89,90,91],"Case history with case type, actions taken and outcome","Payment lifecycle data from the payment hub and tracker","Scheme rulebooks and internal procedures for each case type","Standing settlement instructions and correspondent bank static data",[93,94,95,96,97],"Payment hub or payment engine (repair queues, returns)","SWIFT interface, gpi tracker and case management service","Sanctions and fraud filtering systems (read only)","Case management or CRM for the customer side","Email and secure messaging for counterparties that do not use structured messages",{"steps":99,"guardrails":115,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":127},[100,103,106,109,112],{"title":101,"detail":102},"Map the case types","Take a quarter of cases and group them by type (repair, unable to apply, claim non receipt, recall, fee query, duplicate). Volume and handling time per type decide where to start.",{"title":104,"detail":105},"Automate the reading and the gathering first","Before any drafting, let the AI classify cases and assemble the facts into the case file. This targets the lookup time first and is low risk, because nothing leaves the bank.",{"title":107,"detail":108},"Add drafting with templates","Draft structured messages from the case data and free text only where the counterparty requires it, with operators approving every outgoing message at first.",{"title":110,"detail":111},"Introduce straight through handling per case type","Once a case type shows stable quality, allow the agent to send information requests and chasers on its own, while repairs, recalls and credits keep maker checker approval.",{"title":113,"detail":114},"Close the loop with the customer","Connect the case status to the customer channel so the front office and the customer see the same state without calling operations.",[116,117,118,119],"Maker checker approval on every repair, recall, return or credit adjustment","The agent never overrides or clears a sanctions or fraud hit","Outgoing messages validated against the ISO 20022 schema before sending","Customer updates use approved wording and never speculate on the outcome","Operators approve every action that moves or redirects money and own cases that involve fraud, sanctions or a complaint. Team leads review a sample of automated chasers and closures weekly.",[122,123,124,125,126],"Cases resolved without manual lookup, per case type","Median days to resolution per case type","Operator minutes per case","Share of outgoing messages rejected or queried by counterparties","Customer chasers per case",[128,131,134],{"title":129,"detail":130},"Wrong repair sent at scale","A plausible but wrong field correction sends money to the wrong place. Keep human approval on repairs and validate against static data.",{"title":132,"detail":133},"Automated chasing that annoys counterparties","Duplicate or badly timed chasers damage correspondent relationships. Respect agreed response windows and deduplicate.",{"title":135,"detail":136},"Case file and customer story drift apart","The customer is told something the case does not support. Generate customer updates only from the case status.",{"euAiAct":138,"regulations":141,"guidance":148,"controls":155,"incidents":160},{"tier":139,"basis":140},"minimal","Handling payment exceptions is not a use listed in Annex III and is not a prohibited practice under Article 5. If the agent interacts directly with customers, for example in a chat about the case, Article 50(1) requires that they are told they are interacting with an AI system.",[142,143,144,145,146,147],"eu-ai-act","gdpr","dora","fatf-recommendations","apra-cps-230","eu-psd2",[149],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"Harmonised ISO 20022 data requirements for enhancing cross border payments (updated report)","Committee on Payments and Market Infrastructures","global","https://www.bis.org/cpmi/publ/d230.htm","Data requirements developed with the Payments Market Practice Group, first published in October 2023 and updated in February 2026 with a separate technical annex. They are not regulatory requirements, but the CPMI encourages adoption by the end of 2027. The report lists the ISO 20022 return and investigation messages in its core message set and says the unique end to end transaction reference simplifies exception and investigation handling and enables its automation.",[156,157,158,159],"Maker checker on money movement, with the approver recorded on the case","Full case trail of inputs, drafts, approvals and messages for audit and complaints","Schema validation of every outgoing ISO 20022 message","Monthly quality sample per case type",[],{"howToBuild":162},"On Blits.ai this is an **agentic workflow** that the payment hub or case system starts through\nthe API, with scheduled runs for chasers.\n**Custom functions** call the payment hub, the tracker and the case system through REST, the\n**knowledge base** holds scheme rules and procedures with hybrid retrieval, and the agent drafts\nmessages with **structured output** so they can be validated before sending. With **human in\nthe loop approval** configured for money movement, every repair, recall, return or credit waits\nfor an operator's confirmation before the workflow continues, and a **tool execution policy**\nlimits which tools the agent may call on its own.\n\nThe **email channel** handles counterparties that still reply in free text, and the same case\nstatus can feed a customer facing agent. **Guardrails** and **PII masking** protect customer\ndata in prompts, every run has a **full audit trail**, and **test suites** built from historical\ncases run before a new case type goes live.",[164,167,170],{"question":165,"answer":166},"Does ISO 20022 remove the need for AI in payment investigations?","No, it makes AI more useful. ISO 20022 defines structured exception and investigation messages, which make cases easier to classify and automate when both banks use them, but counterparties can still reply late or in free text, and someone still has to gather the facts, decide on the next step and keep the customer informed.",{"question":168,"answer":169},"Can an AI agent recall or repair a payment on its own?","It should not. The agent can propose the repair or recall with its reasoning and draft the message, but a person approves any action that moves or redirects money, and sanctions or fraud hits are never cleared by the agent.",{"question":171,"answer":172},"What results have banks published?","Very few so far. In a Microsoft customer story, BNY says a digital employee in its Eliza platform repairs missing or incomplete payment instructions and handles over ten percent of its payment repair issues worldwide. J.P. Morgan's AI payment validation screening works one step earlier, on preventing exceptions rather than investigating them. BNY also reports faster handling of client transaction inquiries in the same story, but does not say how many of those are payment investigations rather than other transaction queries.",[174,175,176,177,178,179],"ledger-and-payment-reconciliation","chargeback-and-representment","sanctions-screening-adjudication","corporate-client-servicing-assistant","correspondence-triage-and-routing","scam-payment-interception","2026-09-27",[182],{"date":180,"note":183},"First published","payment-investigations-and-exceptions",[186,211],{"title":187,"useCases":188,"organization":189,"vendors":193,"summary":194,"stage":195,"year":196,"channels":197,"languages":198,"metrics":199,"outcomeDisclosed":191,"sources":200,"verification":206,"grade":208,"id":209,"organizationSlug":210},"J.P. Morgan: AI payment validation screening that cuts account validation rejections",[184],{"name":190,"anonymized":191,"country":192,"region":152,"industry":17},"JPMorgan Chase",false,"US",[],"J.P. Morgan says it uses AI powered large language models for payment validation screening, which it describes as speeding up processing by reducing false positives and enabling better queue management. In November 2023 the bank said it had used this for more than two years and that account validation rejection rates had fallen by 15 to 20 percent, alongside lower fraud and a better customer experience. Screening at validation works on preventing payment exceptions rather than on investigating payments already in trouble.","production",2023,[],[],[],[201],{"url":202,"title":203,"publisher":204,"date":205},"https://www.jpmorgan.com/insights/payments/payments-optimization/ai-payments-efficiency-fraud-reduction","AI Boosting Payments Efficiency & Cutting Fraud | J.P. Morgan","J.P. Morgan","2023-11-20",{"level":207,"checkedAt":180},"source-verified","B","jpmorgan-payment-validation-screening","jpmorgan-chase",{"title":212,"useCases":213,"organization":214,"vendors":218,"summary":224,"stage":195,"year":225,"channels":226,"languages":227,"metrics":229,"outcomeDisclosed":238,"sources":239,"verification":242,"grade":243,"id":244,"organizationSlug":245},"BNY: Eliza digital employee repairs over 10% of payment instructions worldwide",[184],{"name":215,"anonymized":191,"country":192,"region":216,"industry":217},"BNY","north-america","capital-markets",[219,221],{"name":215,"role":220},"in-house",{"name":222,"role":223},"Microsoft","platform","BNY built its own AI platform, Eliza, on Microsoft Azure and Microsoft Foundry. In a Microsoft customer story, a BNY executive describes a digital employee that repairs missing or incomplete payment instructions so the payment can proceed without a time consuming manual review. The same executive says that digital employee now handles over ten percent of BNY's payment repair issues worldwide. The same story describes an agentic workflow for client onboarding research and faster processing of client settlement inquiries, which are reported as separate use cases.",2026,[28],[228],"en",[230],{"kpi":43,"value":231,"unit":232,"qualifier":233,"period":234,"claimant":235,"quote":236,"sourceUrl":237},10,"percent","at-least","payment repair issues worldwide","organization","Today, that digital employee handles over ten percent of our payment repair issues around the world.","https://www.microsoft.com/en/customers/story/27322-bny-microsoft-365-copilot",true,[240],{"url":237,"title":241,"publisher":222},"Frontier Firm BNY resolves client inquires 80% faster with Microsoft AI powered Eliza",{"level":207,"checkedAt":180},"C","bny-eliza-payment-repair-digital-employee","bny",0,[248],{"kpi":43,"label":249,"unit":232,"aggregate":238,"higherIsBetter":238,"n":250,"nUpTo":246,"median":231,"min":231,"max":231,"byClaimant":251,"vendorOnly":191,"points":252},"Automation rate",1,{"organization":250,"vendor":246,"regulator":246,"independent":246},[253],{"evidenceId":244,"organization":215,"value":231,"qualifier":233,"claimant":235,"grade":243,"pooled":238},{"low":255,"high":256},291666.6666666667,2250000,[258,278,292,317,337,360],{"slug":174,"title":259,"shortTitle":260,"definition":261,"status":9,"industries":262,"functions":266,"patterns":268,"audience":31,"autonomy":32,"adoptionStage":270,"segment":31,"evidenceCount":271,"publicEvidenceCount":271,"organizations":272,"bestGrade":208,"headline":277,"lastVerified":180,"indexable":238},"AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI that matches entries across nostro and vostro statements, card and scheme settlement files, the general ledger and suspense accounts, proposes matches and clearing journals, and routes only the genuine breaks to an operator with a plain language explanation.",[17,18,217,263,264,265],"cross-industry","wealth-and-asset-management","government",[267,20],"finance-and-accounting",[23,269,24],"anomaly-detection","early-adopters",4,[273,274,275,276],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",null,{"slug":175,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":284,"patterns":286,"audience":31,"autonomy":32,"adoptionStage":270,"segment":31,"evidenceCount":287,"publicEvidenceCount":288,"organizations":289,"bestGrade":208,"headline":277,"lastVerified":180,"indexable":238},"AI for chargeback and representment operations","Chargeback and representment","AI that runs the dispute engine room for issuers, acquirers and merchants: it maps each dispute to the network reason code, gathers the matching evidence, assembles a network compliant chargeback or representment package, drafts the rebuttal, tracks every deadline and processes pre dispute alerts so a refund can be issued before a chargeback lands.",[18,17,283],"retail-and-ecommerce",[20,285,21],"fraud-prevention",[23,24,26,25],3,2,[290,291],"GitHub","Visa",{"slug":176,"title":293,"shortTitle":294,"definition":295,"status":9,"industries":296,"functions":297,"patterns":299,"audience":31,"autonomy":32,"adoptionStage":270,"segment":301,"evidenceCount":302,"publicEvidenceCount":302,"organizations":303,"bestGrade":208,"headline":311,"lastVerified":316,"indexable":238},"AI for sanctions screening alert adjudication","Sanctions screening adjudication","AI that works the alerts raised when customer, counterparty or payment names match sanctions and watchlists: it resolves fuzzy matches across transliterations, aliases and naming conventions, clears clear non matches with a documented reason, and escalates true or uncertain hits with the evidence attached.",[17,18],[298],"financial-crime-compliance",[25,300,23],"prediction-and-scoring","middle-office",7,[304,305,306,307,308,309,310],"AJ Bell","First National Bank of Omaha (FNBO)","HSBC","Mashreq","Ratepay","Standard Chartered","United Overseas Bank (UOB)",{"kpi":312,"label":313,"unit":232,"n":250,"nUpTo":246,"kind":314,"value":75,"qualifier":315,"claimant":235,"organization":310,"vendorReported":191},"false-positive-reduction","False positive reduction","reported","exact","2026-09-26",{"slug":177,"title":318,"shortTitle":319,"definition":320,"status":9,"industries":321,"functions":322,"patterns":323,"audience":327,"autonomy":32,"adoptionStage":270,"segment":328,"evidenceCount":329,"publicEvidenceCount":288,"organizations":330,"bestGrade":208,"headline":333,"lastVerified":180,"indexable":238},"AI assistant for corporate and commercial client servicing","Corporate client servicing","A conversational assistant inside the corporate banking portal, app and messaging channels that answers finance and treasury teams' servicing questions, such as payment status, balances, cut off times, fees and how to submit an instruction, resolves routine requests end to end and hands the rest to a service specialist who has an AI copilot.",[17,18],[21,20],[324,325,23,326],"conversational-agent","rag-knowledge-assistant","summarization","customer-facing","specialized-businesses",5,[331,332],"Bank of America","DBS Bank",{"kpi":334,"label":335,"unit":232,"n":250,"nUpTo":246,"kind":314,"value":336,"qualifier":315,"claimant":235,"organization":331,"vendorReported":191},"contact-deflection","Contact deflection",16,{"slug":178,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":343,"patterns":345,"audience":31,"autonomy":32,"adoptionStage":346,"segment":31,"evidenceCount":347,"publicEvidenceCount":347,"organizations":348,"bestGrade":208,"headline":355,"lastVerified":180,"indexable":238},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[263,17,342,265],"insurance",[20,21,344],"case-management",[25,24,326],"mainstream",6,[349,350,351,352,353,354],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":356,"label":357,"unit":232,"n":250,"nUpTo":246,"kind":314,"value":358,"qualifier":315,"claimant":359,"organization":353,"vendorReported":238},"accuracy","Accuracy",91,"vendor",{"slug":179,"title":361,"shortTitle":362,"definition":363,"status":9,"industries":364,"functions":365,"patterns":366,"audience":327,"autonomy":32,"adoptionStage":270,"segment":368,"evidenceCount":347,"publicEvidenceCount":347,"organizations":369,"bestGrade":208,"headline":376,"lastVerified":316,"indexable":238},"AI scam intervention for instant payments","Scam payment interception","AI that talks to the customer when they are about to authorise an instant payment that looks like a scam: it combines the payee check and the risk score, asks targeted questions about the payment in plain language, explains the specific scam pattern, and holds, delays or escalates the payment to a human specialist when the risk stays high. Unlike fraud scoring, which stops payments the customer did not make, it protects customers from payments they are being manipulated into making.",[17,18],[285,21],[324,300,23,367],"voice-agent","front-office",[370,371,372,373,374,375],"Commonwealth Bank of Australia","Mastercard","Revolut","Starling Bank","Vodafone","Westpac",{"kpi":377,"label":378,"unit":232,"n":288,"nUpTo":246,"kind":314,"value":379,"qualifier":315,"claimant":359,"organization":373,"vendorReported":238},"detection-rate-improvement","Detection improvement",300,{"indexable":238,"reasons":381},[],[383,390,395,402,409,414,421,428,436,442,448,454,460,466,472,477,484,490,496,502,508,514,519,524,528,535,542,547,552,559,565,571,577,582],{"id":142,"label":384,"issuer":385,"region":386,"url":387,"description":388,"useCases":389,"indexable":238},"EU AI Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":143,"label":391,"issuer":385,"region":386,"url":392,"description":393,"useCases":394,"indexable":238},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":396,"label":397,"issuer":398,"region":152,"url":399,"description":400,"useCases":401,"indexable":238},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":403,"label":404,"issuer":405,"region":216,"url":406,"description":407,"useCases":408,"indexable":238},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":144,"label":410,"issuer":385,"region":386,"url":411,"description":412,"useCases":413,"indexable":238},"DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":415,"label":416,"issuer":417,"region":386,"url":418,"description":419,"useCases":420,"indexable":238},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":422,"label":423,"issuer":424,"region":386,"url":425,"description":426,"useCases":427,"indexable":238},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":429,"label":430,"issuer":431,"region":432,"url":433,"description":434,"useCases":435,"indexable":238},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":146,"label":437,"issuer":438,"region":432,"url":439,"description":440,"useCases":441,"indexable":238},"APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":443,"label":444,"issuer":445,"region":152,"url":446,"description":447,"useCases":60,"indexable":238},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":449,"label":450,"issuer":451,"region":216,"url":452,"description":453,"useCases":60,"indexable":238},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":455,"label":456,"issuer":457,"region":386,"url":458,"description":459,"useCases":336,"indexable":238},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":145,"label":461,"issuer":462,"region":152,"url":463,"description":464,"useCases":465,"indexable":238},"FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":467,"label":468,"issuer":385,"region":386,"url":469,"description":470,"useCases":471,"indexable":238},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":473,"label":474,"issuer":385,"region":386,"url":475,"description":476,"useCases":471,"indexable":238},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":478,"label":479,"issuer":480,"region":216,"url":481,"description":482,"useCases":483,"indexable":238},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":485,"label":486,"issuer":385,"region":386,"url":487,"description":488,"useCases":489,"indexable":238},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":491,"label":492,"issuer":493,"region":216,"url":494,"description":495,"useCases":489,"indexable":238},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":497,"label":498,"issuer":499,"region":152,"url":500,"description":501,"useCases":489,"indexable":238},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":503,"label":504,"issuer":385,"region":386,"url":505,"description":506,"useCases":507,"indexable":238},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":509,"label":510,"issuer":511,"region":216,"url":512,"description":513,"useCases":507,"indexable":238},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":515,"label":516,"issuer":431,"region":432,"url":517,"description":518,"useCases":231,"indexable":238},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":520,"label":521,"issuer":385,"region":386,"url":522,"description":523,"useCases":231,"indexable":238},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":147,"label":525,"issuer":385,"region":386,"url":526,"description":527,"useCases":231,"indexable":238},"PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":529,"label":530,"issuer":531,"region":386,"url":532,"description":533,"useCases":534,"indexable":238},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":536,"label":537,"issuer":538,"region":216,"url":539,"description":540,"useCases":541,"indexable":238},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":543,"label":544,"issuer":385,"region":386,"url":545,"description":546,"useCases":541,"indexable":238},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":548,"label":549,"issuer":385,"region":386,"url":550,"description":551,"useCases":347,"indexable":238},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":553,"label":554,"issuer":555,"region":556,"url":557,"description":558,"useCases":329,"indexable":238},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":560,"label":561,"issuer":562,"region":386,"url":563,"description":564,"useCases":271,"indexable":238},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":566,"label":567,"issuer":568,"region":386,"url":569,"description":570,"useCases":271,"indexable":238},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":572,"label":573,"issuer":574,"region":432,"url":575,"description":576,"useCases":287,"indexable":238},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":578,"label":579,"issuer":385,"region":386,"url":580,"description":581,"useCases":287,"indexable":238},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":583,"label":584,"issuer":585,"region":216,"url":586,"description":587,"useCases":287,"indexable":238},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598300750]